Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations656
Missing cells3123
Missing cells (%)20.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory526.4 KiB
Average record size in memory821.7 B

Variable types

Text6
Numeric11
Categorical4
Boolean1
URL1

Alerts

exclude has constant value "True" Constant
column has constant value "http://www.shadowandact.com/?p=23430" Constant
audience score is highly overall correlated with rotten tomatoesHigh correlation
box office average per cinema is highly overall correlated with domestic gross and 4 other fieldsHigh correlation
budget is highly overall correlated with domestic gross and 4 other fieldsHigh correlation
domestic gross is highly overall correlated with box office average per cinema and 6 other fieldsHigh correlation
foreign gross is highly overall correlated with box office average per cinema and 6 other fieldsHigh correlation
genre is highly overall correlated with idHigh correlation
id is highly overall correlated with genre and 3 other fieldsHigh correlation
lead studio is highly overall correlated with idHigh correlation
number of theatres in opening weekend is highly overall correlated with budget and 5 other fieldsHigh correlation
opening weekend is highly overall correlated with box office average per cinema and 6 other fieldsHigh correlation
profit is highly overall correlated with box office average per cinema and 5 other fieldsHigh correlation
rotten tomatoes is highly overall correlated with audience scoreHigh correlation
story is highly overall correlated with idHigh correlation
worldwide gross is highly overall correlated with box office average per cinema and 6 other fieldsHigh correlation
year is highly overall correlated with idHigh correlation
exclude has 528 (80.5%) missing values Missing
lead studio has 109 (16.6%) missing values Missing
number of theatres in opening weekend has 45 (6.9%) missing values Missing
box office average per cinema has 54 (8.2%) missing values Missing
foreign gross has 55 (8.4%) missing values Missing
budget has 11 (1.7%) missing values Missing
proftitability has 11 (1.7%) missing values Missing
oscar has 640 (97.6%) missing values Missing
bafta has 644 (98.2%) missing values Missing
source has 351 (53.5%) missing values Missing
column has 655 (99.8%) missing values Missing
id is uniformly distributed Uniform
movies_id has unique values Unique
id has unique values Unique

Reproduction

Analysis started2025-09-15 10:05:52.135734
Analysis finished2025-09-15 10:06:35.909159
Duration43.77 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

movies_id
Text

Unique 

Distinct656
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size38.6 KiB
2025-09-15T12:06:35.974846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters7216
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique656 ?
Unique (%)100.0%

Sample

1st rowmovies-0000
2nd rowmovies-0001
3rd rowmovies-0002
4th rowmovies-0003
5th rowmovies-0004
ValueCountFrequency (%)
movies-0000 1
 
0.2%
movies-0020 1
 
0.2%
movies-0009 1
 
0.2%
movies-0002 1
 
0.2%
movies-0003 1
 
0.2%
movies-0004 1
 
0.2%
movies-0005 1
 
0.2%
movies-0006 1
 
0.2%
movies-0007 1
 
0.2%
movies-0008 1
 
0.2%
Other values (646) 646
98.5%
2025-09-15T12:06:36.068705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 892
12.4%
m 656
9.1%
v 656
9.1%
i 656
9.1%
e 656
9.1%
s 656
9.1%
- 656
9.1%
o 656
9.1%
1 236
 
3.3%
4 236
 
3.3%
Other values (7) 1260
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 892
12.4%
m 656
9.1%
v 656
9.1%
i 656
9.1%
e 656
9.1%
s 656
9.1%
- 656
9.1%
o 656
9.1%
1 236
 
3.3%
4 236
 
3.3%
Other values (7) 1260
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 892
12.4%
m 656
9.1%
v 656
9.1%
i 656
9.1%
e 656
9.1%
s 656
9.1%
- 656
9.1%
o 656
9.1%
1 236
 
3.3%
4 236
 
3.3%
Other values (7) 1260
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 892
12.4%
m 656
9.1%
v 656
9.1%
i 656
9.1%
e 656
9.1%
s 656
9.1%
- 656
9.1%
o 656
9.1%
1 236
 
3.3%
4 236
 
3.3%
Other values (7) 1260
17.5%

id
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct656
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean328.5
Minimum1
Maximum656
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2025-09-15T12:06:36.101612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile33.75
Q1164.75
median328.5
Q3492.25
95-th percentile623.25
Maximum656
Range655
Interquartile range (IQR)327.5

Descriptive statistics

Standard deviation189.51517
Coefficient of variation (CV)0.57691072
Kurtosis-1.2
Mean328.5
Median Absolute Deviation (MAD)164
Skewness0
Sum215496
Variance35916
MonotonicityStrictly increasing
2025-09-15T12:06:36.201349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
432 1
 
0.2%
434 1
 
0.2%
435 1
 
0.2%
436 1
 
0.2%
437 1
 
0.2%
438 1
 
0.2%
439 1
 
0.2%
440 1
 
0.2%
441 1
 
0.2%
Other values (646) 646
98.5%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
656 1
0.2%
655 1
0.2%
654 1
0.2%
653 1
0.2%
652 1
0.2%
651 1
0.2%
650 1
0.2%
649 1
0.2%
648 1
0.2%
647 1
0.2%

year
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size33.4 KiB
200
376 
201
280 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201
2nd row201
3rd row201
4th row201
5th row201

Common Values

ValueCountFrequency (%)
200 376
57.3%
201 280
42.7%

Length

2025-09-15T12:06:36.230211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-15T12:06:36.256288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
200 376
57.3%
201 280
42.7%

Most occurring characters

ValueCountFrequency (%)
0 1032
52.4%
2 656
33.3%
1 280
 
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1968
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1032
52.4%
2 656
33.3%
1 280
 
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1968
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1032
52.4%
2 656
33.3%
1 280
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1968
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1032
52.4%
2 656
33.3%
1 280
 
14.2%

exclude
Boolean

Constant  Missing 

Distinct1
Distinct (%)0.8%
Missing528
Missing (%)80.5%
Memory size21.1 KiB
True
128 
(Missing)
528 
ValueCountFrequency (%)
True 128
 
19.5%
(Missing) 528
80.5%
2025-09-15T12:06:36.266910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

film
Text

Distinct653
Distinct (%)99.7%
Missing1
Missing (%)0.2%
Memory size41.3 KiB
2025-09-15T12:06:36.365763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length56
Median length40
Mean length15.29313
Min length1

Characters and Unicode

Total characters10017
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique651 ?
Unique (%)99.4%

Sample

1st row127 Hours
2nd rowA Nightmare on Elm Street
3rd rowAlice in Wonderland
4th rowAll About Steve
5th rowAll Good Things
ValueCountFrequency (%)
the 205
 
11.3%
of 59
 
3.3%
and 25
 
1.4%
a 17
 
0.9%
in 16
 
0.9%
2 15
 
0.8%
to 11
 
0.6%
love 10
 
0.6%
you 10
 
0.6%
i 10
 
0.6%
Other values (1050) 1435
79.2%
2025-09-15T12:06:36.506622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1157
 
11.6%
e 1009
 
10.1%
a 611
 
6.1%
r 588
 
5.9%
o 584
 
5.8%
n 530
 
5.3%
t 509
 
5.1%
i 508
 
5.1%
s 406
 
4.1%
h 395
 
3.9%
Other values (65) 3720
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10017
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1157
 
11.6%
e 1009
 
10.1%
a 611
 
6.1%
r 588
 
5.9%
o 584
 
5.8%
n 530
 
5.3%
t 509
 
5.1%
i 508
 
5.1%
s 406
 
4.1%
h 395
 
3.9%
Other values (65) 3720
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10017
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1157
 
11.6%
e 1009
 
10.1%
a 611
 
6.1%
r 588
 
5.9%
o 584
 
5.8%
n 530
 
5.3%
t 509
 
5.1%
i 508
 
5.1%
s 406
 
4.1%
h 395
 
3.9%
Other values (65) 3720
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10017
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1157
 
11.6%
e 1009
 
10.1%
a 611
 
6.1%
r 588
 
5.9%
o 584
 
5.8%
n 530
 
5.3%
t 509
 
5.1%
i 508
 
5.1%
s 406
 
4.1%
h 395
 
3.9%
Other values (65) 3720
37.1%

lead studio
Categorical

High correlation  Missing 

Distinct46
Distinct (%)8.4%
Missing109
Missing (%)16.6%
Memory size37.5 KiB
Independent
110 
Paramount
55 
Warner Bros.
53 
Universal
46 
Sony
42 
Other values (41)
241 

Length

Max length25
Median length24
Mean length9.8372943
Min length3

Characters and Unicode

Total characters5381
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)3.7%

Sample

1st rowIndependent
2nd rowWarner Bros.
3rd rowDisney
4th rowIndependent
5th rowIndependent

Common Values

ValueCountFrequency (%)
Independent 110
16.8%
Paramount 55
8.4%
Warner Bros. 53
8.1%
Universal 46
7.0%
Sony 42
 
6.4%
Fox 41
 
6.2%
Disney 35
 
5.3%
Independant 32
 
4.9%
Lionsgate 19
 
2.9%
Relativity Media 15
 
2.3%
Other values (36) 99
15.1%
(Missing) 109
16.6%

Length

2025-09-15T12:06:36.536805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
independent 111
15.7%
warner 64
 
9.0%
bros 64
 
9.0%
paramount 55
 
7.8%
fox 50
 
7.1%
universal 46
 
6.5%
sony 43
 
6.1%
disney 35
 
4.9%
independant 32
 
4.5%
relativity 20
 
2.8%
Other values (46) 189
26.7%

Most occurring characters

ValueCountFrequency (%)
n 798
14.8%
e 704
13.1%
a 368
 
6.8%
t 348
 
6.5%
r 346
 
6.4%
d 312
 
5.8%
o 275
 
5.1%
i 251
 
4.7%
s 215
 
4.0%
p 172
 
3.2%
Other values (37) 1592
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5381
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 798
14.8%
e 704
13.1%
a 368
 
6.8%
t 348
 
6.5%
r 346
 
6.4%
d 312
 
5.8%
o 275
 
5.1%
i 251
 
4.7%
s 215
 
4.0%
p 172
 
3.2%
Other values (37) 1592
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5381
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 798
14.8%
e 704
13.1%
a 368
 
6.8%
t 348
 
6.5%
r 346
 
6.4%
d 312
 
5.8%
o 275
 
5.1%
i 251
 
4.7%
s 215
 
4.0%
p 172
 
3.2%
Other values (37) 1592
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5381
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 798
14.8%
e 704
13.1%
a 368
 
6.8%
t 348
 
6.5%
r 346
 
6.4%
d 312
 
5.8%
o 275
 
5.1%
i 251
 
4.7%
s 215
 
4.0%
p 172
 
3.2%
Other values (37) 1592
29.6%

rotten tomatoes
Real number (ℝ)

High correlation 

Distinct100
Distinct (%)15.3%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean48.99084
Minimum0
Maximum99
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2025-09-15T12:06:36.564695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q126
median48
Q372
95-th percentile92
Maximum99
Range99
Interquartile range (IQR)46

Descriptive statistics

Standard deviation26.659191
Coefficient of variation (CV)0.54416685
Kurtosis-1.1594447
Mean48.99084
Median Absolute Deviation (MAD)23
Skewness0.084812409
Sum32089
Variance710.71245
MonotonicityNot monotonic
2025-09-15T12:06:36.595617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 15
 
2.3%
14 15
 
2.3%
27 15
 
2.3%
52 13
 
2.0%
78 12
 
1.8%
19 11
 
1.7%
68 10
 
1.5%
46 10
 
1.5%
38 10
 
1.5%
13 10
 
1.5%
Other values (90) 534
81.4%
ValueCountFrequency (%)
0 1
 
0.2%
1 1
 
0.2%
2 3
 
0.5%
3 2
 
0.3%
4 5
0.8%
5 2
 
0.3%
6 5
0.8%
7 6
0.9%
8 4
0.6%
9 8
1.2%
ValueCountFrequency (%)
99 1
 
0.2%
98 2
 
0.3%
97 3
 
0.5%
96 4
0.6%
95 3
 
0.5%
94 9
1.4%
93 9
1.4%
92 6
0.9%
91 5
0.8%
90 5
0.8%

audience score
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.910061
Minimum19
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2025-09-15T12:06:36.626076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile33
Q147
median59
Q373
95-th percentile87
Maximum96
Range77
Interquartile range (IQR)26

Descriptive statistics

Standard deviation16.579657
Coefficient of variation (CV)0.27674245
Kurtosis-0.78875615
Mean59.910061
Median Absolute Deviation (MAD)13
Skewness-0.015848479
Sum39301
Variance274.88503
MonotonicityNot monotonic
2025-09-15T12:06:36.657186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 21
 
3.2%
50 20
 
3.0%
56 19
 
2.9%
73 19
 
2.9%
57 19
 
2.9%
61 16
 
2.4%
47 16
 
2.4%
55 15
 
2.3%
43 15
 
2.3%
48 15
 
2.3%
Other values (63) 481
73.3%
ValueCountFrequency (%)
19 1
 
0.2%
20 1
 
0.2%
22 1
 
0.2%
24 2
 
0.3%
25 1
 
0.2%
26 2
 
0.3%
27 3
0.5%
28 5
0.8%
29 4
0.6%
31 7
1.1%
ValueCountFrequency (%)
96 1
 
0.2%
93 4
0.6%
92 2
 
0.3%
91 5
0.8%
90 6
0.9%
89 6
0.9%
88 5
0.8%
87 9
1.4%
86 6
0.9%
85 5
0.8%

story
Categorical

High correlation 

Distinct22
Distinct (%)3.4%
Missing2
Missing (%)0.3%
Memory size36.9 KiB
Comedy
87 
Love
73 
Monster Force
63 
Quest
59 
Rivalry
38 
Other values (17)
334 

Length

Max length18
Median length14
Mean length8.4266055
Min length4

Characters and Unicode

Total characters5511
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEscape
2nd rowMonster Force
3rd rowJourney And Return
4th rowComedy
5th rowThe Riddle

Common Values

ValueCountFrequency (%)
Comedy 87
13.3%
Love 73
 
11.1%
Monster Force 63
 
9.6%
Quest 59
 
9.0%
Rivalry 38
 
5.8%
Discovery 36
 
5.5%
Pursuit 32
 
4.9%
Transformation 27
 
4.1%
Revenge 26
 
4.0%
Maturation 26
 
4.0%
Other values (12) 187
28.5%

Length

2025-09-15T12:06:36.688508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
comedy 87
 
10.3%
love 73
 
8.7%
monster 63
 
7.5%
force 63
 
7.5%
quest 59
 
7.0%
rivalry 38
 
4.5%
discovery 36
 
4.3%
pursuit 32
 
3.8%
transformation 27
 
3.2%
revenge 26
 
3.1%
Other values (22) 339
40.2%

Most occurring characters

ValueCountFrequency (%)
e 729
 
13.2%
o 481
 
8.7%
r 430
 
7.8%
t 325
 
5.9%
s 322
 
5.8%
n 267
 
4.8%
i 259
 
4.7%
a 235
 
4.3%
u 232
 
4.2%
d 209
 
3.8%
Other values (27) 2022
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5511
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 729
 
13.2%
o 481
 
8.7%
r 430
 
7.8%
t 325
 
5.9%
s 322
 
5.8%
n 267
 
4.8%
i 259
 
4.7%
a 235
 
4.3%
u 232
 
4.2%
d 209
 
3.8%
Other values (27) 2022
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5511
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 729
 
13.2%
o 481
 
8.7%
r 430
 
7.8%
t 325
 
5.9%
s 322
 
5.8%
n 267
 
4.8%
i 259
 
4.7%
a 235
 
4.3%
u 232
 
4.2%
d 209
 
3.8%
Other values (27) 2022
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5511
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 729
 
13.2%
o 481
 
8.7%
r 430
 
7.8%
t 325
 
5.9%
s 322
 
5.8%
n 267
 
4.8%
i 259
 
4.7%
a 235
 
4.3%
u 232
 
4.2%
d 209
 
3.8%
Other values (27) 2022
36.7%

genre
Categorical

High correlation 

Distinct12
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size35.6 KiB
Comedy
175 
Action
159 
Drama
98 
Horror
50 
Animation
49 
Other values (7)
125 

Length

Max length11
Median length6
Mean length6.4314024
Min length5

Characters and Unicode

Total characters4219
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdventure
2nd rowHorror
3rd rowAdventure
4th rowComedy
5th rowDrama

Common Values

ValueCountFrequency (%)
Comedy 175
26.7%
Action 159
24.2%
Drama 98
14.9%
Horror 50
 
7.6%
Animation 49
 
7.5%
Thriller 33
 
5.0%
Adventure 31
 
4.7%
Romance 22
 
3.4%
Crime 16
 
2.4%
Biography 13
 
2.0%
Other values (2) 10
 
1.5%

Length

2025-09-15T12:06:36.714511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
comedy 175
26.7%
action 159
24.2%
drama 98
14.9%
horror 50
 
7.6%
animation 49
 
7.5%
thriller 33
 
5.0%
adventure 31
 
4.7%
romance 22
 
3.4%
crime 16
 
2.4%
biography 13
 
2.0%
Other values (2) 10
 
1.5%

Most occurring characters

ValueCountFrequency (%)
o 523
12.4%
r 384
 
9.1%
m 365
 
8.7%
i 319
 
7.6%
e 318
 
7.5%
n 315
 
7.5%
a 285
 
6.8%
t 249
 
5.9%
A 239
 
5.7%
d 206
 
4.9%
Other values (16) 1016
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4219
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 523
12.4%
r 384
 
9.1%
m 365
 
8.7%
i 319
 
7.6%
e 318
 
7.5%
n 315
 
7.5%
a 285
 
6.8%
t 249
 
5.9%
A 239
 
5.7%
d 206
 
4.9%
Other values (16) 1016
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4219
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 523
12.4%
r 384
 
9.1%
m 365
 
8.7%
i 319
 
7.6%
e 318
 
7.5%
n 315
 
7.5%
a 285
 
6.8%
t 249
 
5.9%
A 239
 
5.7%
d 206
 
4.9%
Other values (16) 1016
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4219
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 523
12.4%
r 384
 
9.1%
m 365
 
8.7%
i 319
 
7.6%
e 318
 
7.5%
n 315
 
7.5%
a 285
 
6.8%
t 249
 
5.9%
A 239
 
5.7%
d 206
 
4.9%
Other values (16) 1016
24.1%

number of theatres in opening weekend
Real number (ℝ)

High correlation  Missing 

Distinct532
Distinct (%)87.1%
Missing45
Missing (%)6.9%
Infinite0
Infinite (%)0.0%
Mean2698.347
Minimum2
Maximum4468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2025-09-15T12:06:36.739500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile631.5
Q12396
median2840
Q33320
95-th percentile3962
Maximum4468
Range4466
Interquartile range (IQR)924

Descriptive statistics

Standard deviation952.1302
Coefficient of variation (CV)0.35285684
Kurtosis1.1428928
Mean2698.347
Median Absolute Deviation (MAD)465
Skewness-1.09158
Sum1648690
Variance906551.91
MonotonicityNot monotonic
2025-09-15T12:06:36.769491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 8
 
1.2%
2470 3
 
0.5%
3175 3
 
0.5%
2511 3
 
0.5%
3030 3
 
0.5%
3606 3
 
0.5%
3121 3
 
0.5%
2534 3
 
0.5%
2756 3
 
0.5%
2707 2
 
0.3%
Other values (522) 577
88.0%
(Missing) 45
 
6.9%
ValueCountFrequency (%)
2 2
 
0.3%
3 2
 
0.3%
4 8
1.2%
6 2
 
0.3%
9 1
 
0.2%
11 1
 
0.2%
22 1
 
0.2%
29 1
 
0.2%
36 1
 
0.2%
100 1
 
0.2%
ValueCountFrequency (%)
4468 1
0.2%
4380 1
0.2%
4375 1
0.2%
4366 1
0.2%
4362 1
0.2%
4359 1
0.2%
4325 1
0.2%
4285 1
0.2%
4260 1
0.2%
4252 1
0.2%

box office average per cinema
Real number (ℝ)

High correlation  Missing 

Distinct579
Distinct (%)96.2%
Missing54
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean8172.3322
Minimum1052
Maximum93230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2025-09-15T12:06:36.799716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1052
5-th percentile2243.8
Q13785
median5943
Q39741
95-th percentile22282.2
Maximum93230
Range92178
Interquartile range (IQR)5956

Descriptive statistics

Standard deviation7865.2285
Coefficient of variation (CV)0.96242153
Kurtosis29.296118
Mean8172.3322
Median Absolute Deviation (MAD)2567.5
Skewness4.1680711
Sum4919744
Variance61861820
MonotonicityNot monotonic
2025-09-15T12:06:36.830404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4761 3
 
0.5%
3132 2
 
0.3%
4605 2
 
0.3%
1806 2
 
0.3%
9743 2
 
0.3%
3610 2
 
0.3%
4405 2
 
0.3%
7655 2
 
0.3%
5427 2
 
0.3%
3300 2
 
0.3%
Other values (569) 581
88.6%
(Missing) 54
 
8.2%
ValueCountFrequency (%)
1052 1
0.2%
1354 1
0.2%
1459 1
0.2%
1490 1
0.2%
1513 1
0.2%
1559 1
0.2%
1575 1
0.2%
1585 1
0.2%
1625 1
0.2%
1703 1
0.2%
ValueCountFrequency (%)
93230 1
0.2%
61777 1
0.2%
45429 1
0.2%
41890 1
0.2%
41038 1
0.2%
40385 1
0.2%
39384 1
0.2%
38672 1
0.2%
36338 1
0.2%
36283 1
0.2%

domestic gross
Real number (ℝ)

High correlation 

Distinct623
Distinct (%)95.8%
Missing6
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean67.996292
Minimum0
Maximum743.8
Zeros3
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2025-09-15T12:06:36.860042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.427
Q120.22
median40.195
Q388.8675
95-th percentile222.061
Maximum743.8
Range743.8
Interquartile range (IQR)68.6475

Descriptive statistics

Standard deviation77.446419
Coefficient of variation (CV)1.13898
Kurtosis13.121705
Mean67.996292
Median Absolute Deviation (MAD)26.585
Skewness2.8472878
Sum44197.59
Variance5997.9478
MonotonicityNot monotonic
2025-09-15T12:06:36.892059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.7 3
 
0.5%
0 3
 
0.5%
25.93 2
 
0.3%
22.5 2
 
0.3%
24.54 2
 
0.3%
16 2
 
0.3%
9.2 2
 
0.3%
10.3 2
 
0.3%
0.02 2
 
0.3%
0.54 2
 
0.3%
Other values (613) 628
95.7%
(Missing) 6
 
0.9%
ValueCountFrequency (%)
0 3
0.5%
0.02 2
0.3%
0.03 2
0.3%
0.11 1
 
0.2%
0.22 1
 
0.2%
0.38 1
 
0.2%
0.41 1
 
0.2%
0.54 2
0.3%
0.58 1
 
0.2%
0.97 1
 
0.2%
ValueCountFrequency (%)
743.8 1
0.2%
530.92 1
0.2%
415 1
0.2%
402.1 1
0.2%
381.01 1
0.2%
352.39 1
0.2%
336.53 1
0.2%
334.19 1
0.2%
322.72 1
0.2%
319.25 1
0.2%

foreign gross
Real number (ℝ)

High correlation  Missing 

Distinct577
Distinct (%)96.0%
Missing55
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean87.978687
Minimum0
Maximum1969
Zeros6
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2025-09-15T12:06:36.923059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.58
Q111.22
median37.84
Q393.74
95-th percentile389.03
Maximum1969
Range1969
Interquartile range (IQR)82.52

Descriptive statistics

Standard deviation151.33161
Coefficient of variation (CV)1.720094
Kurtosis44.468603
Mean87.978687
Median Absolute Deviation (MAD)32.45
Skewness5.0723883
Sum52875.191
Variance22901.256
MonotonicityNot monotonic
2025-09-15T12:06:36.955456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
0.9%
2.5 2
 
0.3%
16.8 2
 
0.3%
20.8 2
 
0.3%
17 2
 
0.3%
5.5 2
 
0.3%
6.7 2
 
0.3%
5.8 2
 
0.3%
3.1 2
 
0.3%
1.11 2
 
0.3%
Other values (567) 577
88.0%
(Missing) 55
 
8.4%
ValueCountFrequency (%)
0 6
0.9%
0.01 2
 
0.3%
0.02 1
 
0.2%
0.029 1
 
0.2%
0.062 1
 
0.2%
0.12 1
 
0.2%
0.14 2
 
0.3%
0.15 1
 
0.2%
0.17 1
 
0.2%
0.23 2
 
0.3%
ValueCountFrequency (%)
1969 1
0.2%
947.1 1
0.2%
802.8 1
0.2%
770.81 1
0.2%
690.2 1
0.2%
687.9 1
0.2%
660 1
0.2%
651.58 1
0.2%
648.16 1
0.2%
647.88 1
0.2%

worldwide gross
Real number (ℝ)

High correlation 

Distinct634
Distinct (%)97.2%
Missing4
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean151.07402
Minimum0.03
Maximum2712.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2025-09-15T12:06:36.986905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile8.6585
Q134.815
median75.7
Q3176.1825
95-th percentile576.367
Maximum2712.85
Range2712.82
Interquartile range (IQR)141.3675

Descriptive statistics

Standard deviation216.96465
Coefficient of variation (CV)1.436148
Kurtosis33.960663
Mean151.07402
Median Absolute Deviation (MAD)53.495
Skewness4.3536168
Sum98500.26
Variance47073.658
MonotonicityNot monotonic
2025-09-15T12:06:37.017764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03 2
 
0.3%
93.4 2
 
0.3%
63.8 2
 
0.3%
73.8 2
 
0.3%
183.3 2
 
0.3%
1.1 2
 
0.3%
29 2
 
0.3%
73.4 2
 
0.3%
29.37 2
 
0.3%
34.5 2
 
0.3%
Other values (624) 632
96.3%
(Missing) 4
 
0.6%
ValueCountFrequency (%)
0.03 2
0.3%
0.38 1
0.2%
0.41 1
0.2%
0.55 1
0.2%
0.64 1
0.2%
1.07 1
0.2%
1.1 2
0.3%
1.32 1
0.2%
1.57 1
0.2%
2.71 1
0.2%
ValueCountFrequency (%)
2712.85 1
0.2%
1328.11 1
0.2%
1123.2 1
0.2%
1063.16 1
0.2%
1043.87 1
0.2%
1024.39 1
0.2%
996.9 1
0.2%
961 1
0.2%
955.41 1
0.2%
939.88 1
0.2%

budget
Real number (ℝ)

High correlation  Missing 

Distinct135
Distinct (%)20.9%
Missing11
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean54.096147
Minimum0
Maximum300
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2025-09-15T12:06:37.107112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q120
median35
Q370
95-th percentile166.6
Maximum300
Range300
Interquartile range (IQR)50

Descriptive statistics

Standard deviation51.370097
Coefficient of variation (CV)0.94960732
Kurtosis2.7571938
Mean54.096147
Median Absolute Deviation (MAD)20
Skewness1.706458
Sum34892.015
Variance2638.8869
MonotonicityNot monotonic
2025-09-15T12:06:37.135987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 49
 
7.5%
30 33
 
5.0%
25 29
 
4.4%
40 28
 
4.3%
35 25
 
3.8%
15 24
 
3.7%
60 21
 
3.2%
150 20
 
3.0%
50 19
 
2.9%
80 18
 
2.7%
Other values (125) 379
57.8%
ValueCountFrequency (%)
0 1
 
0.2%
0.015 1
 
0.2%
0.2 1
 
0.2%
0.5 1
 
0.2%
1.5 1
 
0.2%
1.7 1
 
0.2%
1.8 1
 
0.2%
2 3
0.5%
2.5 1
 
0.2%
2.6 1
 
0.2%
ValueCountFrequency (%)
300 1
 
0.2%
260 1
 
0.2%
258 1
 
0.2%
250 2
 
0.3%
237 1
 
0.2%
230 1
 
0.2%
210 1
 
0.2%
200 11
1.7%
195 1
 
0.2%
190 1
 
0.2%

profit
Real number (ℝ)

High correlation 

Distinct637
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.963788
Minimum-111.01
Maximum2475.85
Zeros4
Zeros (%)0.6%
Negative107
Negative (%)16.3%
Memory size5.3 KiB
2025-09-15T12:06:37.166152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-111.01
5-th percentile-18.1175
Q16.55
median39.925
Q3117.9225
95-th percentile438.6375
Maximum2475.85
Range2586.86
Interquartile range (IQR)111.3725

Descriptive statistics

Standard deviation182.58967
Coefficient of variation (CV)1.8830708
Kurtosis48.681841
Mean96.963788
Median Absolute Deviation (MAD)39.27
Skewness5.2178514
Sum63608.245
Variance33338.989
MonotonicityNot monotonic
2025-09-15T12:06:37.196938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.5 4
 
0.6%
0 4
 
0.6%
-6.1 2
 
0.3%
38.3 2
 
0.3%
14 2
 
0.3%
11.2 2
 
0.3%
3.3 2
 
0.3%
1.12 2
 
0.3%
26.8 2
 
0.3%
-2.9 2
 
0.3%
Other values (627) 632
96.3%
ValueCountFrequency (%)
-111.01 1
0.2%
-88.5 1
0.2%
-58.8 1
0.2%
-50.5 1
0.2%
-50 1
0.2%
-44.83 1
0.2%
-41.2 1
0.2%
-39.83 1
0.2%
-37.66 1
0.2%
-36.1 1
0.2%
ValueCountFrequency (%)
2475.85 1
0.2%
1203.11 1
0.2%
928.2 1
0.2%
863.16 1
0.2%
830.41 1
0.2%
824.39 1
0.2%
811.9 1
0.2%
794.5 1
0.2%
793.87 1
0.2%
789.88 1
0.2%

proftitability
Text

Missing 

Distinct583
Distinct (%)90.4%
Missing11
Missing (%)1.7%
Memory size34.8 KiB
2025-09-15T12:06:37.301202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.5643411
Min length1

Characters and Unicode

Total characters3589
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique534 ?
Unique (%)82.8%

Sample

1st row337.39%
2nd row330.46%
3rd row512.20%
4th row267.53%
5th row3.20%
ValueCountFrequency (%)
1.93 5
 
0.8%
2.1 4
 
0.6%
0.9 3
 
0.5%
6.83 3
 
0.5%
1.47 3
 
0.5%
1.3 3
 
0.5%
1.07 3
 
0.5%
2.09 3
 
0.5%
2.03 3
 
0.5%
1.73 3
 
0.5%
Other values (572) 612
94.9%
2025-09-15T12:06:37.433093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 642
17.9%
1 374
10.4%
% 360
10.0%
2 338
9.4%
3 313
8.7%
0 269
7.5%
4 233
 
6.5%
5 223
 
6.2%
8 217
 
6.0%
7 214
 
6.0%
Other values (10) 406
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3589
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 642
17.9%
1 374
10.4%
% 360
10.0%
2 338
9.4%
3 313
8.7%
0 269
7.5%
4 233
 
6.5%
5 223
 
6.2%
8 217
 
6.0%
7 214
 
6.0%
Other values (10) 406
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3589
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 642
17.9%
1 374
10.4%
% 360
10.0%
2 338
9.4%
3 313
8.7%
0 269
7.5%
4 233
 
6.5%
5 223
 
6.2%
8 217
 
6.0%
7 214
 
6.0%
Other values (10) 406
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3589
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 642
17.9%
1 374
10.4%
% 360
10.0%
2 338
9.4%
3 313
8.7%
0 269
7.5%
4 233
 
6.5%
5 223
 
6.2%
8 217
 
6.0%
7 214
 
6.0%
Other values (10) 406
11.3%

opening weekend
Real number (ℝ)

High correlation 

Distinct483
Distinct (%)74.3%
Missing6
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean21.275206
Minimum0
Maximum169.19
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2025-09-15T12:06:37.463243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3315
Q17
median13.93
Q326.625
95-th percentile65.951
Maximum169.19
Range169.19
Interquartile range (IQR)19.625

Descriptive statistics

Standard deviation23.515448
Coefficient of variation (CV)1.1052982
Kurtosis9.7180121
Mean21.275206
Median Absolute Deviation (MAD)8.53
Skewness2.7053204
Sum13828.884
Variance552.97628
MonotonicityNot monotonic
2025-09-15T12:06:37.492801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 5
 
0.8%
17.6 5
 
0.8%
6.9 5
 
0.8%
5.4 4
 
0.6%
4.7 4
 
0.6%
10.6 4
 
0.6%
0.11 4
 
0.6%
7 4
 
0.6%
7.6 4
 
0.6%
12.3 4
 
0.6%
Other values (473) 607
92.5%
(Missing) 6
 
0.9%
ValueCountFrequency (%)
0 1
0.2%
0.01 1
0.2%
0.02 2
0.3%
0.032 1
0.2%
0.037 1
0.2%
0.05 2
0.3%
0.075 1
0.2%
0.08 1
0.2%
0.09 2
0.3%
0.097 1
0.2%
ValueCountFrequency (%)
169.19 1
0.2%
158.4 1
0.2%
151.1 1
0.2%
142.8 1
0.2%
138.12 1
0.2%
128.1 1
0.2%
125 1
0.2%
121.6 1
0.2%
116.1 1
0.2%
114.7 1
0.2%

oscar
Text

Missing 

Distinct12
Distinct (%)75.0%
Missing640
Missing (%)97.6%
Memory size21.3 KiB
2025-09-15T12:06:37.538105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length60
Median length36.5
Mean length23.375
Min length8

Characters and Unicode

Total characters374
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)56.2%

Sample

1st rowBest Actress
2nd rowSup. Actor, Sup. Actress
3rd rowBest Picture, Director, Actor, Orig. Screenplay
4th rowOriginal Screenplay
5th rowBest Picture, Director, Supporting Actor, Adapted Screenplay
ValueCountFrequency (%)
best 8
16.7%
actor 7
14.6%
screenplay 6
12.5%
actress 4
8.3%
supporting 4
8.3%
picture 4
8.3%
director 4
8.3%
animated 3
 
6.2%
original 3
 
6.2%
sup 2
 
4.2%
Other values (2) 3
 
6.2%
2025-09-15T12:06:37.606352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 37
 
9.9%
e 37
 
9.9%
t 36
 
9.6%
32
 
8.6%
c 25
 
6.7%
i 22
 
5.9%
p 18
 
4.8%
s 16
 
4.3%
n 16
 
4.3%
A 16
 
4.3%
Other values (15) 119
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 374
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 37
 
9.9%
e 37
 
9.9%
t 36
 
9.6%
32
 
8.6%
c 25
 
6.7%
i 22
 
5.9%
p 18
 
4.8%
s 16
 
4.3%
n 16
 
4.3%
A 16
 
4.3%
Other values (15) 119
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 374
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 37
 
9.9%
e 37
 
9.9%
t 36
 
9.6%
32
 
8.6%
c 25
 
6.7%
i 22
 
5.9%
p 18
 
4.8%
s 16
 
4.3%
n 16
 
4.3%
A 16
 
4.3%
Other values (15) 119
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 374
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 37
 
9.9%
e 37
 
9.9%
t 36
 
9.6%
32
 
8.6%
c 25
 
6.7%
i 22
 
5.9%
p 18
 
4.8%
s 16
 
4.3%
n 16
 
4.3%
A 16
 
4.3%
Other values (15) 119
31.8%

bafta
Text

Missing 

Distinct9
Distinct (%)75.0%
Missing644
Missing (%)98.2%
Memory size21.0 KiB
2025-09-15T12:06:37.649489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length39
Median length26
Mean length18.666667
Min length8

Characters and Unicode

Total characters224
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)58.3%

Sample

1st rowOriginal Screenplay
2nd rowSupporting Actor, Director
3rd rowAnimated
4th rowSupporting Actress
5th rowLeading Actor
ValueCountFrequency (%)
supporting 4
14.8%
actor 4
14.8%
screenplay 4
14.8%
animated 3
11.1%
director 3
11.1%
original 2
7.4%
film 2
7.4%
adapted 2
7.4%
actress 1
 
3.7%
leading 1
 
3.7%
2025-09-15T12:06:37.717102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 21
 
9.4%
e 19
 
8.5%
t 18
 
8.0%
i 17
 
7.6%
15
 
6.7%
p 14
 
6.2%
n 14
 
6.2%
c 12
 
5.4%
a 12
 
5.4%
o 11
 
4.9%
Other values (15) 71
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 21
 
9.4%
e 19
 
8.5%
t 18
 
8.0%
i 17
 
7.6%
15
 
6.7%
p 14
 
6.2%
n 14
 
6.2%
c 12
 
5.4%
a 12
 
5.4%
o 11
 
4.9%
Other values (15) 71
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 21
 
9.4%
e 19
 
8.5%
t 18
 
8.0%
i 17
 
7.6%
15
 
6.7%
p 14
 
6.2%
n 14
 
6.2%
c 12
 
5.4%
a 12
 
5.4%
o 11
 
4.9%
Other values (15) 71
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 21
 
9.4%
e 19
 
8.5%
t 18
 
8.0%
i 17
 
7.6%
15
 
6.7%
p 14
 
6.2%
n 14
 
6.2%
c 12
 
5.4%
a 12
 
5.4%
o 11
 
4.9%
Other values (15) 71
31.7%

source
Text

Missing 

Distinct223
Distinct (%)73.1%
Missing351
Missing (%)53.5%
Memory size41.3 KiB
2025-09-15T12:06:37.799465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length140
Median length107
Mean length52.363934
Min length24

Characters and Unicode

Total characters15971
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique218 ?
Unique (%)71.5%

Sample

1st rowhttp://boxofficemojo.com/movies/?id=127hours.htm
2nd rowhttp://www.the-numbers.com/movies/2009/ABSTV.php
3rd rowhttp://www.wikipedia.org
4th rowhttp://boxofficemojo.com/movies
5th rowhttp://boxofficemojo.com/movies
ValueCountFrequency (%)
http://www.the-numbers.com/movies/records/allbudgets.php 66
 
20.3%
http://boxofficemojo.com/movies 13
 
4.0%
http://www.the-numbers.com/movies/2009/abstv.php 4
 
1.2%
http://latimesblogs.latimes.com/entertainmentnewsbuzz/2011/10/movie-projector-real-steel-ides-of-march.html 2
 
0.6%
http://latimesblogs.latimes.com/entertainmentnewsbuzz/2011/11/muppets-arthur-christmas-hugo-box-office.html 2
 
0.6%
2
 
0.6%
unofficial 1
 
0.3%
for 1
 
0.3%
links 1
 
0.3%
see 1
 
0.3%
Other values (232) 232
71.4%
2025-09-15T12:06:37.909291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1510
 
9.5%
/ 1327
 
8.3%
t 1149
 
7.2%
e 1118
 
7.0%
m 1111
 
7.0%
i 830
 
5.2%
h 790
 
4.9%
. 720
 
4.5%
s 699
 
4.4%
p 602
 
3.8%
Other values (66) 6115
38.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15971
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1510
 
9.5%
/ 1327
 
8.3%
t 1149
 
7.2%
e 1118
 
7.0%
m 1111
 
7.0%
i 830
 
5.2%
h 790
 
4.9%
. 720
 
4.5%
s 699
 
4.4%
p 602
 
3.8%
Other values (66) 6115
38.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15971
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1510
 
9.5%
/ 1327
 
8.3%
t 1149
 
7.2%
e 1118
 
7.0%
m 1111
 
7.0%
i 830
 
5.2%
h 790
 
4.9%
. 720
 
4.5%
s 699
 
4.4%
p 602
 
3.8%
Other values (66) 6115
38.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15971
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1510
 
9.5%
/ 1327
 
8.3%
t 1149
 
7.2%
e 1118
 
7.0%
m 1111
 
7.0%
i 830
 
5.2%
h 790
 
4.9%
. 720
 
4.5%
s 699
 
4.4%
p 602
 
3.8%
Other values (66) 6115
38.3%

column
URL

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing655
Missing (%)99.8%
Memory size20.7 KiB
http://www.shadowandact.com/?p=23430
 
1
(Missing)
655 
ValueCountFrequency (%)
http://www.shadowandact.com/?p=23430 1
 
0.2%
(Missing) 655
99.8%
ValueCountFrequency (%)
http 1
 
0.2%
(Missing) 655
99.8%
ValueCountFrequency (%)
www.shadowandact.com 1
 
0.2%
(Missing) 655
99.8%
ValueCountFrequency (%)
/ 1
 
0.2%
(Missing) 655
99.8%
ValueCountFrequency (%)
p=23430 1
 
0.2%
(Missing) 655
99.8%
ValueCountFrequency (%)
1
 
0.2%
(Missing) 655
99.8%

Interactions

2025-09-15T12:06:33.529410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:05:52.448728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-15T12:06:06.537532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:08.514512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:15.443719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:22.227054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:29.298656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:31.405574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:34.016138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:05:53.283056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:00.175584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:02.364200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:04.904335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:06.938711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:09.381719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:16.348927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:23.168723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:29.780371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:31.910425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:34.041798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-15T12:06:00.198153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:02.423219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:04.931964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:06.962523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:09.867086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:16.742588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:23.593675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:29.804634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:31.934121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:34.104594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-15T12:06:00.261187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-15T12:06:10.377514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:17.269667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-15T12:06:29.867512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:31.997947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:34.129941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:05:55.119671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:00.284532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:02.576032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:05.029090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-15T12:06:10.826832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:18.003659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:24.631808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:29.891042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-15T12:06:30.352651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:32.545936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:35.113830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:05:57.309567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:01.276877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:03.671910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:05.978912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:07.981248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:12.943326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:20.060901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:26.915981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:30.834919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:33.013112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:35.619970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:05:58.245737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:01.779557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:04.234716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:06.462337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:08.440804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:13.901287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:20.902205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:27.815843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:31.332682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:33.456464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:35.645510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:05:58.653559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:01.803817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:04.297916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:06.487429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:08.464993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:14.429060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:21.348370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:28.314775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:31.356648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:33.480858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:35.670373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:05:59.215102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:01.826574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:04.373074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:06.511881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:08.488199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:14.845788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:21.798405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:28.803908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:31.380251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-15T12:06:33.503012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-15T12:06:37.929578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
audience scorebox office average per cinemabudgetdomestic grossforeign grossgenreidlead studionumber of theatres in opening weekendopening weekendprofitrotten tomatoesstoryworldwide grossyear
audience score1.0000.4350.1620.4070.3590.000-0.0180.0000.1600.2700.3970.6700.1250.3690.000
box office average per cinema0.4351.0000.3950.7660.6070.108-0.0410.0000.4720.7760.6980.2820.0670.7030.000
budget0.1620.3951.0000.6500.6970.149-0.0180.1460.7210.6440.422-0.0030.1280.7090.000
domestic gross0.4070.7660.6501.0000.8020.052-0.0360.0000.7650.9250.8660.1600.0860.9380.147
foreign gross0.3590.6070.6970.8021.0000.000-0.0740.2190.7410.7710.8460.1300.0890.9410.128
genre0.0000.1080.1490.0520.0001.0001.0000.0570.1910.1200.1350.0910.3500.0000.000
id-0.018-0.041-0.018-0.036-0.0741.0001.0001.000-0.064-0.021-0.061-0.0641.000-0.0541.000
lead studio0.0000.0000.1460.0000.2190.0571.0001.0000.1110.0000.2120.0540.0860.1060.404
number of theatres in opening weekend0.1600.4720.7210.7650.7410.191-0.0640.1111.0000.8230.635-0.0230.1240.7790.190
opening weekend0.2700.7760.6440.9250.7710.120-0.0210.0000.8231.0000.7990.0330.0870.8860.000
profit0.3970.6980.4220.8660.8460.135-0.0610.2120.6350.7991.0000.1790.1160.9100.000
rotten tomatoes0.6700.282-0.0030.1600.1300.091-0.0640.054-0.0230.0330.1791.0000.0610.1300.000
story0.1250.0670.1280.0860.0890.3501.0000.0860.1240.0870.1160.0611.0000.0210.124
worldwide gross0.3690.7030.7090.9380.9410.000-0.0540.1060.7790.8860.9100.1300.0211.0000.109
year0.0000.0000.0000.1470.1280.0001.0000.4040.1900.0000.0000.0000.1240.1091.000

Missing values

2025-09-15T12:06:35.717427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-15T12:06:35.766539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-15T12:06:35.845825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

movies_ididyearexcludefilmlead studiorotten tomatoesaudience scorestorygenrenumber of theatres in opening weekendbox office average per cinemadomestic grossforeign grossworldwide grossbudgetprofitproftitabilityopening weekendoscarbaftasourcecolumn
0movies-00001201None127 HoursIndependent93.084EscapeAdventure916.02333.018.3342.460.7318.042.73337.39%0.260NoneNonehttp://boxofficemojo.com/movies/?id=127hours.htmNone
1movies-00012201NoneA Nightmare on Elm StreetWarner Bros.13.040Monster ForceHorror3332.09875.063.0852.59115.6635.080.66330.46%32.900NoneNoneNoneNone
2movies-00023201NoneAlice in WonderlandDisney52.072Journey And ReturnAdventure3728.031143.0334.19690.21024.39200.0824.39512.20%116.100NoneNoneNoneNone
3movies-00034201NoneAll About SteveIndependent6.035ComedyComedy2251.04994.033.866.2640.1315.025.13267.53%11.200NoneNonehttp://www.the-numbers.com/movies/2009/ABSTV.phpNone
4movies-00045201TrueAll Good ThingsIndependent33.064The RiddleDrama2.0NaN0.580.0620.6420.0-19.363.20%0.037NoneNonehttp://www.wikipedia.orgNone
5movies-00056201NoneAlpha and OmegaCrest17.041Journey And ReturnAnimation2625.03469.025.124.829.9120.09.91149.55%9.100NoneNoneNoneNone
6movies-00067201TrueBarry MundayIndependent43.043MaturationComedyNaNNaNNoneNoneNoneNaN0.00NoneNaNNoneNoneNoneNone
7movies-00078201NoneBlack SwanFox88.086Wretched ExcessDrama959.08742.0106.95222.44329.3913.0316.392533.77%8.380Best ActressNoneNoneNone
8movies-00089201NoneBrooklyn's FinestIndependent42.047TemptationAction1936.06896.027.29.1536.3117.019.31213.59%13.400NoneNonehttp://boxofficemojo.com/moviesNone
9movies-000910201NoneBuriedIndependent86.063EscapeDrama11.09115.01.0417.3318.382.016.38919.00%0.103NoneNonehttp://boxofficemojo.com/moviesNone
movies_ididyearexcludefilmlead studiorotten tomatoesaudience scorestorygenrenumber of theatres in opening weekendbox office average per cinemadomestic grossforeign grossworldwide grossbudgetprofitproftitabilityopening weekendoscarbaftasourcecolumn
646movies-0646647200NoneUpDisney98.086Journey And ReturnAnimation3766.018085.0293.0434727.1175.0552.104.1568.10AnimatedAnimatedhttp://www.the-numbers.com/movies/2009/UP.phpNone
647movies-0647648200NoneUp in the AirParamount90.076MaturationDrama1895.05947.083.8278.2162.0225.0137.026.4811.20NoneAdapted Screenplayhttp://boxofficemojo.com/movies/?id=upintheair.htmNone
648movies-0648649200NoneWatchmenWarner Bros.64.068SacrificeAction3611.015291.0107.577.7185.3138.047.301.3455.20NoneNonehttp://www.the-numbers.com/movies/records/allbudgets.phpNone
649movies-0649650200TrueWhatever WorksNone50.063DiscoveryComedy9.029574.05.323.729.015.014.001.930.26NoneNonehttp://en.wikipedia.org/wiki/Whatever_WorksNone
650movies-0650651200NoneWhere the Wild Things AreWarner Bros.73.059Journey And ReturnAdventure3735.08754.063.416.385.3100.0-14.700.8532.70NoneNonehttp://www.the-numbers.com/movies/records/allbudgets.phpNone
651movies-0651652200TrueWhip ItNone84.073MaturationDrama1721.02702.013.0316.015.01.001.074.70NoneNonehttp://www.boxofficemojo.com/movies/?id=whipit.htmNone
652movies-0652653200TrueWhiteoutNone7.028PursuitAction2745.01791.010.31.912.235.0-22.800.354.90NoneNonehttp://www.the-numbers.com/movies/records/allbudgets.phpNone
653movies-0653654200NoneX-Men Origins: WolverineFox37.072RevengeAction4099.020751.0179.9193.2373.1150.0223.102.4985.10NoneNonehttp://www.the-numbers.com/movies/records/allbudgets.phpNone
654movies-0654655200TrueYear OneNone14.031QuestAdventure3022.06489.032.426.260.260.00.20119.60NoneNonehttp://www.the-numbers.com/movies/records/allbudgets.phpNone
655movies-0655656200NoneZombielandSony90.087Monster ForceAction3036.08147.049.242.593.323.669.703.9524.70NoneNonehttp://www.the-numbers.com/movies/records/allbudgets.phpNone